Massive amounts of data are created by research, medicine, and society through large-scale scientific simulations, high-throughput and multi-modal experimental devices, web and social media usage, and ever increasing number of sensors in the environment, manufacturing, and even in our daily lives. From engineering to medicine, manufacturing to social sciences, the challenge has shifted from generating sufficient amounts of data to understanding and using it, hence resulting in the rapid emergence of the field of Data Science. CSE faculty are prominent leaders in the interdisciplinary field of Data Science, particularly in developing new data and visual analytics approaches to analyze and transform large and complex data sets into knowledge and actionable information.
Specific topic areas of interest within CSE include:
- Data mining and visualization
- Data and AI security
- Graph analytics
- Health analytics
- Network science
- Social and urban computing
- Explainable and equitable AI
Current interdisciplinary research directions at CSE include developing scalable, interactive and interpretable tools for large-scale data and AI/ML models; understanding and managing dynamical systems, varying from understanding and fighting against the spread of diseases to improving urban infrastructure and strengthening safety and well-being on the web.
Research in data science and visual analytics at Georgia Tech involves many campus units — spanning colleges, schools, and individual labs. Together, these researchers work to create new collaborative opportunities, strengthen partnerships with industry and government, and maximize the societal impact of the transformative data science research conducted at Georgia Tech.
Interdisciplinary CSE Faculty specializing in data science and visual analytics research: